Ebay, Incorporated

Giridharan Iyengar

Robinson Piramuthu

Mohammadhadi Kiapour

Shuai Zheng

Nicholas Whyte

Padmapriya Gudipati

Ashmeet Singh Rekhi

Timothy Keefer

Md Atiq Ul Islam

Bryan Allen Plummer

Alberto Ordonez Pereira

Ravindra Surya Lanka

Suchitra Ramesh

Shane Lin

John Weigel

Niaz Ahamed Khaja Nazimudeen

Fujun Zhong

Kenneth Clark Crookston

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

Computer vision, user segment, and missing item determination

Computer vision and image characteristic search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and comparing the determined characteristics to visual characteristics of other images, e.g., to identify similar visual characteristics in the other images. In some aspects, the described system performs searches that leverage a digital image as part of a search query to locate digital content of interest. In some aspects, the described system surfaces multiple user interface instrumentalities that include images of patterns, textures, or materials and that are selectable to initiate a visual search of digital content having a similar pattern, texture, or material. The described aspects also include pattern-based authentication in which the system determines authenticity of an item in an image based on a similarity of its visual characteristics to visual characteristics of known authentic items.

Computer vision and image characteristic search

Computer vision for unsuccessful queries and iterative search is described. The described system leverages visual search techniques by determining visual characteristics of objects depicted in images and describing them, e.g., using feature vectors. In some aspects, these visual characteristics are determined for search queries that are identified as not being successful. Aggregated information describing visual characteristics of images of unsuccessful search queries is used to determine common visual characteristics and objects depicted in those images. This information can be used to inform other users about unmet needs of searching users. In some aspects, these visual characteristics are used in connection with iterative image searches where users select an initial query image and then the search results are iteratively refined. Unlike conventional techniques, the described system iteratively refines the returned search results using an embedding space learned from binary attribute labels describing images.

Computer vision for unsuccessful queries and iterative search

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San Jose, CA, United States of America

Giridharan Iyengar